server: tests: adding OAI compatible embedding with multiple inputs

examples/server/tests/features/server.feature

@@ -60,6 +60,19 @@ Feature: llama.cpp server
     """
     Then embeddings are generated
 
+  Scenario: OAI Embeddings compatibility with multiple inputs
+    Given a model tinyllama-2
+    Given a prompt:
+      """
+      In which country Paris is located ?
+      """
+    And a prompt:
+      """
+      Is Madrid the capital of Spain ?
+      """
+    When an OAI compatible embeddings computation request for multiple inputs
+    Then embeddings are generated
+
 
   Scenario: Tokenize / Detokenize
     When tokenizing:

examples/server/tests/features/steps/steps.py

@@ -1,4 +1,5 @@
 import asyncio
+import collections
 import json
 import os
 import re
@@ -321,7 +322,15 @@ async def step_compute_embedding(context):
 
 @step(u'embeddings are generated')
 def step_assert_embeddings(context):
+    if len(context.prompts) == 0:
         assert_embeddings(context.embeddings)
+    else:
+        assert len(context.embeddings) == len(context.prompts), (f"unexpected response:\n"
+                                                                 f"context.prompts={context.prompts}\n"
+                                                                 f"context.embeddings={context.embeddings}")
+        for embedding in context.embeddings:
+            context.prompts.pop()
+            assert_embeddings(embedding)
 
 
 @step(u'an OAI compatible embeddings computation request for')
@@ -333,6 +342,15 @@ async def step_oai_compute_embeddings(context):
                                                       model=context.model)
 
 
+@step(u'an OAI compatible embeddings computation request for multiple inputs')
+@async_run_until_complete
+async def step_oai_compute_embeddings_multiple_inputs(context):
+    context.embeddings = await request_oai_embeddings(context.prompts,
+                                                      base_url=context.base_url,
+                                                      user_api_key=context.user_api_key,
+                                                      model=context.model)
+
+
 @step(u'concurrent embedding requests')
 @async_run_until_complete()
 async def step_concurrent_embedding_requests(context):
@@ -607,10 +625,17 @@ async def request_oai_embeddings(input,
     else:
         openai.api_key = user_api_key
         openai.api_base = f'{base_url}/v1'
-        embeddings = openai.Embedding.create(
+        oai_embeddings = openai.Embedding.create(
             model=model,
             input=input,
         )
+
+        if isinstance(input, collections.abc.Sequence):
+            embeddings = []
+            for an_oai_embeddings in oai_embeddings.data:
+                embeddings.append(an_oai_embeddings.embedding)
+        else:
+            embeddings = oai_embeddings.data.embedding
         return embeddings